Several effective medication treatments are now available for patients with bipolar disorder. Lithium is the first mood stabilizer and the one for which there is the greatest efficacy data. Many patients tolerate this medication well, respond well and are stabilized on it. Others do not respond or tolerate the medication. Research indicates that the diversity in lithium treatment response may have significant genetic determinants. We will create a valuable database for an association study of genetic single nucleotide polymorphisms (SNPs) in an existing patient set in which subjects were treated with lithium for bipolar disorder. In this existing patient set, we will survey SNPs in more than 20 genes that are relevant to lithium response based on literature data and our prior research. This database will then serve as the basis for developing a model to predict lithium treatment outcome response. Using state of the art methods, we will carefully select important SNPs to build the model from in context of other important SNPs and clinical characteristics of the patients. The model will allow simple interactions to be expressed as well as complex interactions between SNPs in different genes, including interactions with clinical characteristics. The model will be robustly tested using state of the art methodology to estimate its performance in new patients. Further, we will conduct a study to obtain a set of 80 new patients that have bipolar disorder and were treated with lithium. We will use this patient set to further validate the model developed from the existing patient set. This will allow weaknesses and strengths of the model to be characterized and allow it to be extended to develop a diagnostic for assessing individual bipolar patients lithium treatment response. (Relevance) A genetically based diagnostic to predict lithium treatment outcome response for individual patients with bipolar disorder will provide bipolar patients and their physicians with guidance for drug selection, resulting in positive impacts on life expectancy, as well as significant reductions in patient suffering from repeat episodes, suicide rates, and long-term healthcare costs. [unreadable] [unreadable] [unreadable]